Ovarian cancer comprises a diverse set of malignancies that are challenging to detect and to treat successfully. The disease often goes undetected until advanced stages, when it has metastasized throughout the abdominal cavity. Improvements in treatment have been hindered by limited understanding of the disease biology and its molecular heterogeneity. Identifying targeted therapeutic strategies and stratifying treatment based on a patient's individual tumor biology is sine qua non to improving clinical outcomes. This project focuses on understanding the potential of the estrogen receptor-alpha (ER?, for simplicity called ER throughout the proposal) as a therapeutic target in ovarian cancer. ER is an established driver of several cancer types including breast cancer and targeting estrogen action is the primary therapeutic strategy for breast cancer patients with ER-positive disease. Importantly, ER is expressed in the majority (~80%) of ovarian tumors and epidemiologic evidence supports a role for estrogen in ovarian tumorigenesis. Moreover, clinical trials suggest a subset of ovarian cancer patients can be successfully treated with endocrine therapy. To determine the role of ER in ovarian cancer, I will utilize molecular biology approaches to identify what genes drive ER-mediated growth and survival in ovarian cancer cells in vitro and in vivo. Additionally, I will determine if estrogen is required for tumor growthin a unique, clinically relevant mouse model of ovarian cancer. Further, I will evaluate expression of ER-regulated genes in our mouse model to determine if these could be potential biomarkers of endocrine response. Finally, I will measure expression of these putative biomarkers in clinical specimens from ovarian cancer patients who received endocrine therapy to determine if expression correlates with patient outcome. Successful completion of this proposal will establish the mechanism of ER signaling in ovarian cancer, determine if specific biomarkers correlate with estrogen dependence in vivo, and determine if expression of specific genes can be used to predict response to endocrine therapy. Identifying predictive biomarkers for response to endocrine therapy will allow clinicians to prospectively identify patients who will benefit from this treatment. These findings may spur addition clinical trials of endocrine therapy in ovarian cancer and have significant ramifications for ovarian cancer treatment.